Determining percent solids in suspension using raman spectroscopy

ABSTRACT

Methods and apparatus are provided for determining weight percent of solids in a suspension using Raman spectroscopy. The methods can be utilized to acquire Raman spectral data from the suspension and to determine weight percent of solids in a process being carried out, for example, in a vessel, without the need to remove samples for analysis. The weight percent of the solids can be determined with a desired accuracy in a relatively short time, typically 10 minutes or less. The acquired Raman spectral data may be processed by chemometric software using, for example, a partial least squares algorithm and data pretreatment to provide a predicted value of weight percent solids. In some embodiments, the invention is used to determine the weight percent of microparticles of a diketopiperazine in an aqueous solution.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/696,650, filed Nov. 7, 2012, which is a national stage ofPCT/US2011/035112, filed May 4, 2011, which claims priority based onProvisional Application Ser. No. 61/332,292, filed May 7, 2010, whichare hereby incorporated by reference in their entirety.

FIELD OF THE INVENTION

This invention relates to methods and apparatus for measuring percentsolids of particles in a suspension using Raman spectroscopy. Theinvention can be used to provide real time results in a commercialmanufacturing environment, but is not limited to such use.

BACKGROUND OF THE INVENTION

A variety of processes for making products, including products in thefood and pharmaceutical industries, utilize steps with particles insuspension for use in a variety of applications. In one example, it hasbeen proposed to deliver certain types of drugs to patients byinhalation of powder particles as a delivery mechanism. One particularexample uses microparticles comprising diketopiperazine, known asTechnosphere® microparticles. The Technosphere microparticles have aplatelet surface structure and can be loaded with a drug. One use ofthese microparticles is for the delivery of insulin by inhalation.

An exemplary process for making Technosphere microparticles begins withraw materials, including acetic acid and fumaryl diketopiperazine (FDKP)to precipitate the Technosphere particles out of solution to form asuspension. Using a tangential flow filter, the particles are washedusing diafiltration, and the concentration of the particles is increasedby removing liquid. Insulin is added to the suspension to form aTechnosphere Insulin (Tl) suspension. The suspension is flash frozen tomake pellets that are dried in a bulk lyophilization process to removethe liquid components. Dry TI powder removed from the lyophilizer ispacked into containers for later filling of inhaler cartridges.

During manufacturing of Technosphere Insulin, a process tank receivesthe suspension of Technosphere particles after the tangential flowfiltration and concentration steps at approximately 10% solids byweight. The suspension is stirred continuously prior to and duringinsulin addition. Insulin solution preparation does not proceed until anactual percent solids value of the suspension is determined.

Existing methods for determining percent solids of Technosphereparticles in the suspension are slow and are subject to errors. Oneexisting method involves taking one or more samples of the Technospheresuspension, drying the suspension in a microwave oven and weighing theremaining solids. The process typically requires two to three hours andis subject to measurement errors.

Accordingly, there is a need for improved methods and apparatus fordetermining percent of solids in a suspension.

SUMMARY OF THE INVENTION

Embodiments of the invention provide methods and apparatus fordetermining weight percent of solids in a suspension using Ramanspectroscopy. The methods can be utilized to acquire Raman spectral datafrom the suspension and to determine weight percent of solids in aprocess being carried out, for example, in a vessel, without the need toremove samples for analysis. The weight percent of the solids can bedetermined with a desired accuracy in a relatively short time, typically10 minutes or less. The acquired Raman spectral data may be processed,for example, by chemometric software using a partial least squaresalgorithm and data pretreatment, to provide a predicted value of weightpercent solids. In some embodiments, the invention is used to determinethe weight percent of microparticles of a diketopiperazine in an aqueoussolution. The invention is particularly useful for determining weightpercent of solids in a range of 5% to 20%, but is not limited to thisrange.

According to a first embodiment, a method is provided for determiningweight percent of solids in a suspension. The method comprises mixingthe suspension, acquiring Raman spectral data of the solids in thesuspension, and processing the Raman spectral data to determine weightpercent of the solids in the suspension.

According to a second embodiment, an apparatus is provided fordetermining weight percent of solids in a suspension. The apparatuscomprises a Raman spectrometer to acquire Raman spectral data of thesolids in the suspension, and a computing device including a processorand a computer-readable storage medium, the computer-readable storagemedium containing computer instructions that, when executed by theprocessor, perform a method of processing the Raman spectral data todetermine weight percent of the solids in the suspension.

According to a third embodiment, a method is provided for determiningweight percent of solids. The method comprises mixing a suspension todistribute the solids; acquiring spectral data from the suspensionutilizing a probe and a Raman spectrometer; processing the spectraldata; and determining weight percent of the solids in the suspension. Inthis embodiment, the probe utilized to acquire the Raman spectral datacan be used in situ, wherein the probe can be immersed in thesuspension. In one embodiment, the probe can be an optical device or anoptical sensor, such as a short focus optical device. In anotherembodiment, the probe can be used without being immersed in thesuspension, for example, the probe can comprise, for example, a beamthat can penetrate the suspension; the beam can generate a Ramanspectrum from the particles. The Raman spectrum generated by theparticles can be acquired by the spectrophotometer. In yet anotherembodiment, Raman spectral data can be acquired with another type ofoptical sensor.

BRIEF DESCRIPTION OF THE DRAWINGS

For a better understanding of the present invention, reference is madeto the accompanying drawings, which are incorporated herein by referenceand in which:

FIG. 1 is a schematic block diagram of a system for determining weightpercent of solids in a suspension, in accordance with embodiments of theinvention;

FIG. 2 is a flow diagram of a process for determining weight percent ofsolids in a suspension, in accordance with embodiments of the invention;

FIG. 3 is a flow diagram that illustrates processing of the Ramanspectral data to determine weight percent of solids in a suspension, inaccordance with embodiments of the invention;

FIG. 4 is a tabulation of Raman spectrometer parameters used todetermine weight percent of solids in a suspension, in accordance withembodiments of the invention;

FIG. 5 is a tabulation of model parameters and results for determiningweight percent of solids in a suspension, in accordance with embodimentsof the invention;

FIG. 6 is a graph that illustrates a calibration model, in accordancewith embodiments of the invention; and

FIG. 7 is a graph that illustrates model validation, in accordance withembodiments of the invention.

DETAILED DESCRIPTION

Embodiments of the invention provide methods and apparatus fordetermining weight percent of solids in a suspension using Ramanspectroscopy. The methods can be utilized to determine weight percent ofsolids in a suspension without the need to remove samples for analysis.The weight percent of solids can be determined with a desired accuracyin a relatively short time, typically ten minutes or less.

The components of a system for determining weight percent of solids in asuspension, in accordance with embodiments of the invention, are shownin FIG. 1. A vessel 100 contains a suspension 102 of solid particles forwhich the weight percent of solids is to be measured. A mixing device110 ensures that the suspension 102 is mixed, at least duringacquisition of Raman spectral data. The vessel 100 may be any suitablesize and may be a component of a production process.

While FIG. 1 shows vessel 100, the suspension can be in any type ofcontainer, such as a vessel, flask, beaker or pipe, during acquisitionof Raman spectral data. The container can have any size and shape. Insome embodiments, the suspension is in a process vessel or pipe, andRaman spectral data is acquired before, during and/or after a process.In other embodiments, the suspension is in a test container, such as abeaker or flask, and Raman spectral data is acquired off-line or in alaboratory environment. Mixing the suspension may entail variousmechanisms. In the process, the suspension may be mixed, stirred, causedto flow, or otherwise agitated in the container. This ensures that thedistribution of particles in the region of Raman spectral dataacquisition is more or less uniform and is representative of the processbeing analyzed.

In one specific example, suspension 102 is an aqueous suspension ofparticles comprising a compound represented by the formula 1 such as adiketopiperazine. As used herein, “diketopiperazine” or “DKP” includesdiketopiperazines and salts, derivatives, analogs and modificationsthereof falling within the scope of the general Formula 1, wherein thering atoms E1 and E2 at positions 1 and 4 are either O or N and at leastone of the side-chains R₁ and R₂ located at positions 3 and 6respectively contains a carboxylic acid (carboxylate) group. Compoundsaccording to Formula 1 include, without limitation, diketopiperazines,diketomorpholines and diketodioxanes and their substitution analogs.

Diketopiperazines can be formed by cyclodimerization of amino acid esterderivatives, as described by Katchalski et al. (J. Amer. Chem. Soc.68:879-80; 1946), by cyclization of dipeptide ester derivatives, or bythermal dehydration of amino acid derivatives in high-boiling solvents,as described by Kopple, et al., (J. Org. Chem. 33:862-64;1968), theteachings of which are incorporated herein.

Methods for synthesis and preparation of diketopiperazines are wellknown to one of ordinary skill in the art and are disclosed in U.S. Pat.Nos. 5,352,461; 5,503,852; 6,071,497; 6,331,318; 6,428,771 and U.S.Patent Publication No. 2006/0040953. U.S. Pat. Nos. 6,444,226 and6,652,885, describe preparing and providing microparticles ofdiketopiperazines in aqueous suspension to which a solution of activeagent is added in order to bind the active agent to the particle. Thesepatents further describe a method of removing a liquid medium bylyophilization to yield microparticles comprising an active agent.Altering the solvent conditions of such suspension to promote binding ofthe active agent to the particle is taught in U.S. Patent ApplicationSer. Nos. 60/717,524 and 11/532,063 both entitled “Method of DrugFormulation Based on Increasing the Affinity of Active Agents forCrystalline Microparticle Surfaces”; and Ser. No. 11/532,065 entitled“Method of Drug Formulation Based on Increasing the Affinity of ActiveAgents for Crystalline Microparticle Surfaces”. See also U.S. Pat. No.6,440,463 and U.S. patent application Ser. No. 11/210,709 filed on Aug.23, 2005 and U.S. patent application Ser. No. 11/208,087. In someinstances, it is contemplated that the loaded diketopiperazine particlesof the present invention are dried by a method of spray drying asdisclosed in, for example, U.S. patent application Ser. No. 11/678,046filed on Feb. 22, 2006 and entitled “A Method For Improving thePharmaceutic Properties of Microparticles Comprising Diketopiperazineand an Active Agent.” Each of these patents and patent applications isincorporated by reference herein for all they contain regardingdiketopiperazines.

Diketopiperazines, in addition to making aerodynamically suitablemicroparticles, can also facilitate the delivery of active agents byrapidly dissolving at physiologic pH thereby releasing the active agentand speeding its absorption into the circulation. Diketopiperazines canbe formed into particles that incorporate a drug or particles onto whicha drug can be adsorbed. The combination of a drug and a diketopiperazinecan impart improved drug stability. These particles can be administeredby various routes of administration. As dry powders these particles canbe delivered by inhalation to specific areas of the respiratory system,depending on particle size. Additionally, the particles can be madesmall enough for incorporation into an intravenous suspension dosageform. Oral delivery is also possible with the particles incorporatedinto a suspension, tablets or capsules.

In one embodiment, the diketopiperazine is3,6-bis[4-(N-carboxy-2-propenyl)amidobutyl]-2,5-diketopiperazine or3,6-di(fumaryl-4-aminobutyl)-2,5-diketopiperazine (fumaryldiketopiperazine, FDKP). The FDKP can comprise microparticles in itsacid form or salt forms which can be aerosolized or administered in asuspension.

In another embodiment, the DKP is a derivative of3,6-di(4-aminobutyl)-2,5-diketopiperazine, which can be formed by(thermal) condensation of the amino acid lysine. Exemplary derivativesinclude 3,6-di(succinyl-4-aminobutyl)-2,5-diketopiperazine,3,6-di(maleyl-4-aminobutyl)-2,5-diketopiperazine,3,6-di(glutaryl-4-aminobutyl)-2,5-diketo-piperazine,3,6-di(malonyl-4-aminobutyl)-2,5-diketopiperazine,3,6-di(oxalyl-4-amino-butyl)-2,5-diketopiperazine,3,6-di(fumaryl-4-aminobutyl)-2,5- diketopiperazine or3,6-di(citraconyl-4-aminobutyl)-2,5-diketopiperazine and derivativestherefrom. The use of DKPs for drug delivery is known in the art (seefor example U.S. Pat. Nos. 5,352,461, 5,503,852, 6,071,497, and6,331,318, each of which is incorporated herein by reference for allthat it teaches regarding diketopiperazines anddiketopiperazine-mediated drug delivery). The use of DKP salts isdescribed in co-pending U.S. patent application Ser. No. 11/210,710filed Aug. 23, 2005, which is hereby incorporated by reference for allit teaches regarding diketopiperazine salts. Pulmonary drug deliveryusing DKP microparticles is disclosed in U.S. Pat. No. 6,428,771, whichis hereby incorporated by reference in its entirety. Further detailsrelated to adsorption of active agents onto crystalline DKP particlescan be found in co-pending U.S. patent application Ser. Nos. 11/532,063and 11/532,065, which are hereby incorporated by reference in theirentirety.

In one embodiment, the microparticles in suspension can have a diameteror size greater than 0.05 micrometers. In a specific embodiment whereinthe microparticles comprise a diketopiperazine, the microparticles ofdiketopiperazine, for example, can comprise FDKP having a size that canbe in a range of 0.1 to 34 micrometers, but the particle size is notlimited to this size range. The weight percent of the solid particles inthe suspension is typically in a range of about 5% to 20%, but theinvention is not limited to this range.

The system for determining the weight percent of solids in thesuspension 102 includes a Raman spectrometer 120, a probe 122 associatedwith Raman spectrometer 120 and a computer 130 executing chemometricsoftware for processing Raman spectral data acquired by Ramanspectrometer 120 and providing a predicted value 132 of the weightpercent of solids in suspension 102. Probe 122 may be connected to Ramanspectrometer 120 by an optical fiber cable 124. Probe tip 126 of probe122 may be inserted into suspension 102 in vessel 100. Probe tip 126may, for example, be sealed in an opening in the wall of vessel 100.

Raman spectrometer 120 includes a laser 140 having an output that istransmitted via optical fiber cables 124 through probe 122 to vessel 100for excitation of the particles in suspension 102. The Raman radiationstimulated by laser 140 is transmitted through probe 122 via opticalfiber cables 124 and optical elements 142 to a detector 144. The opticalelements may include a notch filter 146, a slit 148 and a grating 150 toselect a particular wavelength range (Raman shift) for detection. Thedetector 144 determines the received intensity over the selectedwavelength range to provide a Raman spectrum. In one embodiment, theRaman spectrometer 120 may be a model RXN-3 commercially available fromKaiser Optical Systems Inc., and the probe 122 may be a 0.5 inch shortfocus immersion optic, also available from Kaiser Optical Systems Inc.

The computer 130 can be a personal computer with a Windows XP operatingsystem. In another embodiment, a microprocessor capable of acquiringRaman spectral data and processing the data obtained can be used. Thecomputer 130 may include a non-transitory computer-readable storagemedium for storage of Raman software, chemometric software, or both.Computer-readable storage media includes volatile and nonvolatile,removable and non-removable media implemented in any method ortechnology for storage of information such as computer-readableinstructions, data structures, program modules or other data.Computer-readable storage media includes RAM, ROM, EEPROM, flash memoryor other memory technology, CD-ROM, digital versatile disks (DVD) orother optical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other medium which canbe used to store the desired information and which can be accessed by aprocessor of computer 130.

The Raman software associated with data acquisition can be Hologramssoftware, version 4.1. The chemometric software for processing the Ramanspectral data and providing a predicted value of the weight percent ofsolids in suspension can be Unscrambler software, version 9.8. It willbe understood that other software packages or custom developed softwaremay be utilized within the scope of the invention. Examples ofparameters used for Raman spectral data acquisition and for achemometric model for the example of an aqueous suspension of particlescomprising FDKP are described below.

A flow diagram of a process 200 for determining weight percent of solidsin a suspension, according to embodiments of the invention, is shown inFIG. 2. In act 210, the suspension to be analyzed is mixed, typically bystirring. The mixing produces a relatively uniform distribution ofparticles in the suspension. The mixing may continue at least untilacquisition of Raman spectral data has been completed, though this maynot be necessary, for example, as with slowly (with respect to the timerequired to acquire sufficient data for analysis) settling particles. Inan industrial setting, the mixing process can be continuous.

In act 212, the Raman spectrometer 120 and probe 122 are used to acquireRaman spectral data from the mixed suspension 102 in vessel 100. Asdiscussed below, the Raman spectral data is acquired over one or morespectral ranges. The detector 144 of Raman spectrometer 120 detects theRaman shift produced by suspension 102 in response to the energy fromlaser 140 and records the spectral data in a memory in computer 130.Raman parameters for acquisition of Raman spectral data fromTechnosphere particles are discussed below.

In act 214, the chemometric software in computer 130 processes theacquired Raman spectral data according to a chemometric model. Thechemometric model provides calibration information to be used by thechemometric software in processing the Raman spectral data. Thecalibration information may include reference data which is obtained bya gravimetric measurement method. The chemometric model is selected toprovide a highly accurate prediction of the weight percent of solids insuspension 102 over a range of expected conditions. The chemometricmodel is discussed below. In act 216, the chemometric software outputs apredicted weight percent value based on the acquired Raman spectraldata.

An embodiment of the processing of act 214 is shown in FIG. 3. Datapretreatment of the Raman spectral data may be performed in act 310 toremove or minimize spectral variations. The result of data pretreatmentmay be processed by partial least squares regression in act 312 using amodel as described below.

By way of example only, embodiments of the invention may be used todetermine weight percent of solids in an aqueous suspension of particlescomprising FDKP. In a typical application, the particles comprising FDKPare in a size range of 0.1 to 34 micrometers, and the weight percent ofsolids is in a range of 10% to 12%. More generally, embodiments of theinvention may be used to determine weight percent of solids insuspensions in a range of 5% to 20%, but the invention is not limited tothis range. It will be understood that different Raman spectrometerparameters and model parameters may be used for analyzing differentparticle types, different particle sizes and/or different weight percentranges.

Examples of parameters used by the Raman spectrometer 120 and itsoperating software for acquisition of Raman spectral data from anaqueous suspension of particles comprising FDKP are summarized in FIG.4. As shown in FIG. 4, the laser wavelength may be 785 nanometers, andthe Raman shift region may be 0.00 cm^(−1 to) 3450.00 cm⁻¹. Thewavenumber re-sampling interval may be 0.3 cm⁻¹. In one embodiment, fordetection of particles comprising FDKP, the wavenumber regions ofinterest may be 1180-1800 cm⁻¹ and 2782-3136 cm⁻¹ as shown in FIG. 5. Itwill be understood that different wavenumber regions of interest may beused for sampling of different particle types, based on the chemistryand physical properties of the particles.

In the example of FIG. 4, the exposure time may be 45 seconds, thenumber of accumulations may be four, and cosmic ray filtration may beutilized. The exposure time is the interval during which the detector144 is made available to receive the Raman signal. The accumulationnumber is the quantity of discrete exposure intervals. Cosmic rayfiltration is a software process that removes erratic signals that mayarise during sample analysis due to energetic cosmic ray particlesinteracting with the detector. The total time of analysis for a sampleis the accumulation number multiplied by the exposure time multiplied bytwo (for cosmic ray filtration). Thus, in the example of FIG. 4, thetotal time for analysis of a sample is 6 minutes. The analysis time istypically less than 10 minutes. The parameters can be selected such thatan optimal balance is achieved between the total time of analysis withrequirements for real-time in-situ measurement and sufficient signal tonoise.

The chemometric model utilized by computer 130 in processing the Ramanspectral data in this example is summarized in FIG. 5. As shown, thechemometric model is based on Unscrambler software, version 9.8, acommercially available chemometric software package. In this embodiment,the chemometric model employs a partial least squares algorithm withdata pretreatment. In some embodiments, the Raman spectral data isprocessed by executing a regression algorithm including partial leastsquares, multiple linear regression, or principal component regression,with or without data pretreatment.

The data pretreatment utilized may be Savitzky-Golay first derivativedata pretreatment. It will be understood that different datapretreatment techniques can be utilized. Other data pretreatmenttechniques may include, but are not limited to, multiplicative andextended multiplicative scatter correction, standard normal variate,various derivatives and normalizations. In some embodiments, nopretreatment of the data is required.

The model data is centered, the model size is full, the validationmethod is leverage correction and maximum principal components (PCs) isset to a value of 9. Typically, more PCs than would be expected arechosen. Model size indicates the number of result matrices available fordisplay after calibration, i.e. residuals, variance plot, etc. Leveragecorrection validation method provides an estimate of prediction quality,i.e., root mean square error (RMSE) values. The parameter R² is acorrelation coefficient output by the regression calculation.

In one embodiment, the number of PCs selected minimizes variance withoutoverfitting. The user can verify the validity of the number of PCsselected by inspecting the residual validation variance curve. Thenumber of PCs is selected at or just above the characteristic elbow seenin the plot of Y variance versus PC number. The residual varianceobserved utilizing two PCs in this example is 0.009 units. The degree towhich the model is overfitting the data can be evaluated by theperformance of the model in predicting new samples.

Mathematical pretreatment, or preprocessing of various types can beutilized to remove or minimize undesired spectral variations. Spectralvariations may occur as a result of variation in many different physicalproperties, including but not limited to particle size, refractiveindex, temperature, etc.

The chemometric model was developed by acquiring Raman spectra ofparticles comprising FDKP in suspensions at concentrations ranging fromapproximately 7.00 to 16.00 weight percent. The process samples wereconcentrated and diluted appropriately. Reference percent solids data(Y-data) was measured for each sample by microwave oven loss on dryingmethodology (reference gravimetric methodology), where % S=100*(dryweight/wet weight). Raman spectral data (X-data) are imported into theUnscrambler software, version 9.8 with the reference % S values(Y-data). Partial least squares (PLS) processing is then performed.

The measured reference data (Y-data) is used to develop a calibrationmodel for the chemometric software. The calibration model is designedsuch that processing of the Raman spectral data (X-data) produces apredicted value of weight percent of solids that matches the measuredreference data (Y-data). A plot of the calibration model with theoptimum number of PCs is shown in FIG. 6. The measured Y values areplotted on the horizontal axis, and the predicted Y values are plottedon the vertical axis.

Certain parameters are given after a PLS calibration has been performed,which can be utilized to evaluate the quality of the model. Ifimprovement in quality is desired, omitting spectral data from certainwavenumber regions of the PLS calibration and mathematical pretreatmentare often utilized. The model is evaluated with spectra of samples notutilized in the model development, referred to as validation.

A plot of model validation results is shown in FIG. 7. The measuredreference data is plotted on the horizontal axis, and the predictedvalue is plotted on the vertical axis. Performance over a range of 10%solids to 12% solids was evaluated. The data in FIG. 6 demonstrates thataccuracy, as estimated from RMSE, may be greater than 90%. Depending onthe type of information needed for a particular suspension, loweraccuracies may be acceptable, which depend on application requirements.

EXAMPLE 1

This experiment was performed to measure and determine the percent solidin a suspension consisting of Technosphere (FDKP) particles in water(aqueous suspension). A Raman spectrometer model RXN3 (Kaiser OpticalSystems, Inc.) was used. The Raman Spectrometer was set using theparameters as described in FIG. 4. The Technosphere suspension wasplaced in a 20 ml beaker and stirred during the Raman spectral sampling.Spectral sampling consisted of 8 sample accumulations of 45 secondseach. Four of the eight accumulations were performed for cosmic rayfiltration. The Raman spectra acquired were transferred to theUnscrambler (chemometric software) and analyzed according to the modelas shown in FIG. 3, Step 214. The Raman spectral data obtained was usedto generate a partial least square calibration model with correspondingsample data on percent solid obtained using a reference microwave losson drying technique.

The percent accuracy was determined by dividing the root mean squareerror (RMSE) generated by the output over the mean percent solid valueobtained for all samples and subtracting from 100. The formula for RMSEis given as:

${{RMSE}(P)} = \sqrt{\frac{\sum\limits_{i = 1}^{n}\; \left( {y_{a} - y_{p}} \right)^{2}}{n}}$

where y_(a) is the measured (reference) value, y_(p) is the predictedvalue, and n is the number of observations.

A resultant calibration model is shown in FIG. 6. Table 1 shows thecalibration model reference data of predicted and measured valuesobtained.

TABLE 1 Measured Predicted Sample No. (Reference) Value Value 1 7.107.12 2 8.64 8.53 3 10.10 9.99 4 11.11 11.03 5 12.01 11.89 6 13.19 13.177 14.64 14.74 8 15.96 15.93 9 7.08 7.18 10 8.66 8.62 11 10.01 9.90 1211.12 11.10 13 11.73 11.66 14 13.00 13.01 15 14.45 14.46 17 16.00 15.9618 7.05 7.24 19 8.50 8.62 20 10.03 10.03 21 11.15 11.17 22 12.07 12.0523 13.04 13.13 24 14.83 14.76 25 15.89 16.04 26 10.13 10.04 27 10.4810.50 28 13.20 13.30 Mean 11.52 11.52

The data show that the percent accuracy for measuring solids in asuspension using the present techniques is 99.27% within the range of 7to 16% solids.

Having thus described several aspects of several embodiments of thisinvention, it is to be appreciated that various alterations,modifications, and improvements will readily occur to those skilled inthe art. Such alterations, modifications, and improvements are intendedto be part of this disclosure, and are intended to be within the spiritand scope of the invention. Accordingly, the foregoing description anddrawings are by way of example only.

1. A method for determining weight percent of solids in a suspension,comprising: acquiring Raman spectral data from the suspension containingthe solids; and processing the Raman spectral data to determine weightpercent of the solids in the suspension.
 2. A method as defined in claim1, wherein processing the Raman spectral data includes executing apartial least squares algorithm with Savitzky-Golay first derivativedata pretreatment.
 3. A method as defined in claim 1, wherein processingthe Raman spectral data includes executing a partial least squaresalgorithm using two principal components.
 4. A method as defined inclaim 1, wherein the Raman spectral data is processed by a computingdevice executing a partial least squares algorithm with datapretreatment.
 5. A method as defined in claim 1, wherein processing theRaman spectral data includes executing a regression algorithm comprisinga partial least squares, multiple linear regression, or principalcomponent regression, with or without data pretreatment.
 6. A method fordetermining weight percent of diketopiperazine microparticles in asuspension, comprising: acquiring Raman spectral data from thesuspension while the diketopiperazine microparticles are in thesuspension; and processing the Raman spectral data to determine weightpercent of the diketopiperazine microparticles in the suspension.
 7. Amethod as defined in claim 6, wherein the microparticles have a size ina range of 0.1 to 34 micrometers.
 8. A method as defined in claim 6,wherein acquiring Raman spectral data includes acquiring data in a rangeof 1180 to 1800 cm⁻¹ and a range of 2782 to 3136 cm⁻¹.
 9. A method asdefined in claim 6, wherein processing the Raman spectral data includesexecuting a partial least squares algorithm with Savitzky-Golay firstderivative data pretreatment.
 10. A method as defined in claim 6,wherein processing the Raman spectral data includes executing a partialleast squares algorithm using two principal components.
 11. A method asdefined in claim 6, wherein the Raman spectral data is processed by acomputing device executing a partial least squares algorithm with datapretreatment.
 12. A method as defined in claim 6, wherein processing theRaman spectral data includes executing a regression algorithm comprisinga partial least squares, multiple linear regression, or principalcomponent regression, with or without data pretreatment.
 13. A method asdefined in claim 12, wherein the data pretreatment is selected frommultiplicative and extended multiplicative scatter correction, standardnormal variate, various derivatives, and normalizations.
 14. A methodfor determining weight percent of diketopiperazine microparticles in asuspension, comprising: acquiring Raman spectral data in a range of 1180to 1800 cm⁻¹ and a range of 2782 to 3136 cm⁻¹ from an aqueous suspensioncontaining the diketopiperazine microparticles; and determining theweight percent of the diketopiperazine microparticles in the suspensionbased on the acquired Raman spectral data.
 15. A method as defined inclaim 14, wherein the microparticles have a size in a range of 0.1 to 34micrometers.
 16. A method as defined in claim 14, wherein determiningthe weight percent includes executing a partial least squares algorithmwith Savitzky-Golay first derivative data pretreatment.
 17. A method asdefined in claim 14, wherein determining the weight percent includesexecuting a partial least squares algorithm using two principalcomponents.
 18. A method as defined in claim 14, wherein the Ramanspectral data is processed by a computing device executing a partialleast squares algorithm with data pretreatment.
 19. A method as definedin claim 14, wherein determining the weight percent includes executing aregression algorithm comprising a partial least squares, multiple linearregression, or principal component regression, with or without datapretreatment.
 20. A method as defined in claim 19, wherein the datapretreatment is selected from multiplicative and extended multiplicativescatter correction, standard normal variate, various derivatives, andnormalizations.
 21. A method as defined in claim 14, further comprisingmixing the aqueous suspension until acquisition of the Raman spectraldata is completed.
 22. A method as defined in claim 14, whereindetermining the weight percent includes using a calibration model toprovide a predicted value of weight percent.